@article {Campestrini:Eckhard:Rui:Bazanella:2014,
	title = {Identifiability Analysis and Prediction Error Identification of Anaerobic Batch Bioreactors},
	journal = {Journal of Control, Automation and Electrical Systems},
	volume = {25},
	number = {4},
	year = {2014},
	note = {n/a},
	pages = {438{\textendash}447},
	abstract = {<p>This paper presents the identifiability analysis of a nonlinear model for a batch bioreactor and the estimation of the identifiable parameters within the prediction error framework. The output data of the experiment are the measurements of the methane gas generated by the process, during 37 days, and knowledge of the initial conditions is limited to the initial quantity of chemical oxygen demand. It is shown by the identifiability analysis that only three out of the eight model parameters can be identified with the available measurements and that identification of the remaining parameters would require further knowledge of the initial conditions. A prediction error algorithm is implemented for the estimation of the identifiable parameters. This algorithm is iterative, relies on the gradient of the prediction error, whose calculation is implemented recursively, and consists of a combination of two classic optimization methods: the conjugated gradient method and the Gauss?Newton method.</p>
},
	issn = {2195{\textendash}3880},
	doi = {10.1007/s40313-014-0129-3},
	author = {L. Campestrini and D. Eckhard and R. Rui and A. S. Bazanella}
}
